A quota sample is a type of non-probability sample in which the researcher selects people according to some fixed standard. That is, units are selected into a sample on the basis of pre-specified characteristics so that the total sample has the same distribution of characteristics assumed to exist in the population being studied.

For example, if you are a researcher conducting a national quota sample, you might need to know what proportion of the population is male and what proportion is female, as well as what proportions of each gender fall into different age categories, categories of race and ethnicity, and level of education, among others.

If you collected a sample with the same proportions as these categories within the national population, you would have a quota sample.

### How to Make a Quota Sample

In quota sampling, the researcher aims to represent the major characteristics of the population by sampling a proportional amount of each. For example, if you wanted to obtain a proportional quota sample of 100 people based on gender, you would need to start with an understanding of the man/woman ratio in the larger population. If you found the larger population includes 40 percent women and 60 percent men, you would need a sample of 40 women and 60 men, for a total of 100 respondents. You would start sampling and continue until your sample reached those proportions and then you would stop. If you had already included 40 women in your study, but not 60 men, you would continue to sample men and discard any additional women respondents because you have already met your quota for that category of participants.

### Advantages

Quota sampling is advantageous in that it can be fairly quick and easy to assemble a quota sample locally, which means it has the benefit of time-saving within the research process. A quota sample can also be achieved on a low budget because of this. These features make quota sampling a useful tactic for field research.

### Drawbacks

Quota sampling has several drawbacks. First, the quota frame—or the proportions in each category—must be accurate. This is often difficult because it can be hard to find up-to-date information on certain topics. For example, U.S. Census data is often not published until well after the data was collected, making it possible for some things to have changed proportions between data collection and publication.

Second, the selection of sample elements within a given category of the quota frame may be biased even though the proportion of the population is accurately estimated. For instance, if a researcher set out to interview five people who met a complex set of characteristics, he or she might introduce bias into the sample by avoiding or including certain people or situations. If the interviewer studying a local population avoided going to homes that looked particularly run-down or visited only homes with swimming pools, for example, their sample would be biased.

### An Example of the Quota Sampling Process

Let’s say that we want to understand more about the career goals of students at University X. In particular, we want to look at the differences in career goals between freshmen, sophomores, juniors, and seniors to examine how career goals might change over the course of a college education.

University X has 20,000 students, which is our population. Next, we need to find out how our population of 20,000 students is distributed among the four class categories that we are interested in. If we discover that there are 6,000 freshmen students (30 percent), 5,000 sophomore students (25 percent), 5,000 junior students (25 percent), and 4,000 senior students (20 perecent), this means that our sample must also meet these proportions. If we want to sample 1,000 students, this means that we must survey 300 freshmen, 250 sophomores, 250 juniors, and 200 seniors. We would then continue to randomly select these students for our final sample.

*Updated by Nicki Lisa Cole, Ph.D.*